Overview

Dataset statistics

Number of variables25
Number of observations832
Missing cells3072
Missing cells (%)14.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory162.6 KiB
Average record size in memory200.2 B

Variable types

Numeric18
Categorical7

Alerts

publication_date has a high cardinality: 221 distinct valuesHigh cardinality
contact_name has a high cardinality: 161 distinct valuesHigh cardinality
seller_name has a high cardinality: 83 distinct valuesHigh cardinality
cadastral_geocode has a high cardinality: 307 distinct valuesHigh cardinality
region_geocode has a high cardinality: 243 distinct valuesHigh cardinality
region_capital has a high cardinality: 64 distinct valuesHigh cardinality
region_capital_geocode has a high cardinality: 64 distinct valuesHigh cardinality
views is highly overall correlated with shown_by_daysHigh correlation
price_value is highly overall correlated with seller_nameHigh correlation
shown_by_days is highly overall correlated with viewsHigh correlation
region_square is highly overall correlated with criminality and 6 other fieldsHigh correlation
population is highly overall correlated with grp and 3 other fieldsHigh correlation
grp is highly overall correlated with population and 3 other fieldsHigh correlation
criminality is highly overall correlated with region_square and 2 other fieldsHigh correlation
km_to_msk is highly overall correlated with cadastral_long and 4 other fieldsHigh correlation
km_to_region_capital is highly overall correlated with seller_nameHigh correlation
cadastral_lat is highly overall correlated with region_lat and 3 other fieldsHigh correlation
cadastral_long is highly overall correlated with region_square and 5 other fieldsHigh correlation
region_lat is highly overall correlated with km_to_msk and 4 other fieldsHigh correlation
region_long is highly overall correlated with region_square and 5 other fieldsHigh correlation
region_capital_lat is highly overall correlated with cadastral_lat and 3 other fieldsHigh correlation
region_capital_long is highly overall correlated with region_square and 5 other fieldsHigh correlation
seller_name is highly overall correlated with price_value and 7 other fieldsHigh correlation
region_capital is highly overall correlated with region_square and 11 other fieldsHigh correlation
region_capital_geocode is highly overall correlated with region_square and 11 other fieldsHigh correlation
square has 401 (48.2%) missing valuesMissing
cadastral_geocode has 453 (54.4%) missing valuesMissing
region_geocode has 19 (2.3%) missing valuesMissing
distance has 528 (63.5%) missing valuesMissing
km_to_msk has 9 (1.1%) missing valuesMissing
km_to_region_capital has 519 (62.4%) missing valuesMissing
cadastral_lat has 518 (62.3%) missing valuesMissing
cadastral_long has 518 (62.3%) missing valuesMissing
region_lat has 19 (2.3%) missing valuesMissing
region_long has 19 (2.3%) missing valuesMissing
square has 54 (6.5%) zerosZeros

Reproduction

Analysis started2023-01-29 03:05:52.496933
Analysis finished2023-01-29 03:06:13.051624
Duration20.55 seconds
Software versionpandas-profiling vv3.6.2
Download configurationconfig.json

Variables

asset_id
Real number (ℝ)

Distinct831
Distinct (%)100.0%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean544818.12
Minimum100496
Maximum998040
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.6 KiB
2023-01-29T06:06:13.099442image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum100496
5-th percentile140259.5
Q1315455.5
median547990
Q3768314.5
95-th percentile940113
Maximum998040
Range897544
Interquartile range (IQR)452859

Descriptive statistics

Standard deviation260732.4
Coefficient of variation (CV)0.47856778
Kurtosis-1.2158684
Mean544818.12
Median Absolute Deviation (MAD)226900
Skewness-0.021867698
Sum4.5274386 × 108
Variance6.7981383 × 1010
MonotonicityNot monotonic
2023-01-29T06:06:13.167653image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
814114 1
 
0.1%
641505 1
 
0.1%
697762 1
 
0.1%
537171 1
 
0.1%
342401 1
 
0.1%
201353 1
 
0.1%
447714 1
 
0.1%
905193 1
 
0.1%
629226 1
 
0.1%
400422 1
 
0.1%
Other values (821) 821
98.7%
ValueCountFrequency (%)
100496 1
0.1%
101443 1
0.1%
104555 1
0.1%
105515 1
0.1%
106909 1
0.1%
107052 1
0.1%
108898 1
0.1%
110222 1
0.1%
110724 1
0.1%
112385 1
0.1%
ValueCountFrequency (%)
998040 1
0.1%
996980 1
0.1%
996019 1
0.1%
994039 1
0.1%
994022 1
0.1%
993964 1
0.1%
990213 1
0.1%
987572 1
0.1%
983435 1
0.1%
982025 1
0.1%

views
Real number (ℝ)

Distinct350
Distinct (%)42.1%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean227.46931
Minimum14
Maximum2311
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.6 KiB
2023-01-29T06:06:13.229749image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile33.5
Q1129
median154
Q3278.5
95-th percentile610.5
Maximum2311
Range2297
Interquartile range (IQR)149.5

Descriptive statistics

Standard deviation227.62373
Coefficient of variation (CV)1.0006788
Kurtosis19.563133
Mean227.46931
Median Absolute Deviation (MAD)47
Skewness3.6393965
Sum189027
Variance51812.563
MonotonicityNot monotonic
2023-01-29T06:06:13.286946image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
143 17
 
2.0%
153 15
 
1.8%
164 14
 
1.7%
137 14
 
1.7%
142 14
 
1.7%
150 11
 
1.3%
149 11
 
1.3%
157 10
 
1.2%
132 9
 
1.1%
144 9
 
1.1%
Other values (340) 707
85.0%
ValueCountFrequency (%)
14 3
0.4%
15 5
0.6%
16 2
 
0.2%
17 1
 
0.1%
19 1
 
0.1%
20 2
 
0.2%
22 1
 
0.1%
23 5
0.6%
24 3
0.4%
25 7
0.8%
ValueCountFrequency (%)
2311 1
0.1%
2000 1
0.1%
1662 1
0.1%
1535 1
0.1%
1502 1
0.1%
1398 1
0.1%
1286 1
0.1%
1100 1
0.1%
1088 1
0.1%
1072 1
0.1%

publication_date
Categorical

Distinct221
Distinct (%)26.6%
Missing1
Missing (%)0.1%
Memory size6.6 KiB
25 июля 2022
324 
15 ноября 2022
106 
27 мая 2021
 
17
05 апреля 2022
 
11
24 мая 2021
 
8
Other values (216)
365 

Length

Max length6438
Median length5863
Mean length43.056558
Min length11

Characters and Unicode

Total characters35780
Distinct characters128
Distinct categories14 ?
Distinct scripts3 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique139 ?
Unique (%)16.7%

Sample

1st row15 ноября 2022
2nd row31 марта 2022
3rd row18 апреля 2022
4th row25 июня 2022
5th row17 июня 2022

Common Values

ValueCountFrequency (%)
25 июля 2022 324
38.9%
15 ноября 2022 106
 
12.7%
27 мая 2021 17
 
2.0%
05 апреля 2022 11
 
1.3%
24 мая 2021 8
 
1.0%
24 ноября 2022 7
 
0.8%
01 сентября 2022 7
 
0.8%
15 декабря 2022 6
 
0.7%
06 сентября 2021 5
 
0.6%
15 марта 2021 5
 
0.6%
Other values (211) 335
40.3%

Length

2023-01-29T06:06:13.339862image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2022 652
 
15.0%
июля 349
 
8.0%
25 342
 
7.9%
ноября 148
 
3.4%
2021 139
 
3.2%
15 132
 
3.0%
декабря 72
 
1.7%
сентября 45
 
1.0%
мая 43
 
1.0%
по 37
 
0.9%
Other values (568) 2377
54.8%

Most occurring characters

ValueCountFrequency (%)
3505
 
9.8%
2 3072
 
8.6%
0 1303
 
3.6%
s 1200
 
3.4%
я 1156
 
3.2%
а 958
 
2.7%
- 947
 
2.6%
о 945
 
2.6%
e 938
 
2.6%
i 835
 
2.3%
Other values (118) 20921
58.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 20833
58.2%
Decimal Number 6425
 
18.0%
Space Separator 3505
 
9.8%
Other Punctuation 1771
 
4.9%
Math Symbol 1521
 
4.3%
Dash Punctuation 957
 
2.7%
Uppercase Letter 556
 
1.6%
Connector Punctuation 124
 
0.3%
Other Symbol 26
 
0.1%
Open Punctuation 22
 
0.1%
Other values (4) 40
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 1200
 
5.8%
я 1156
 
5.5%
а 958
 
4.6%
о 945
 
4.5%
e 938
 
4.5%
i 835
 
4.0%
t 827
 
4.0%
и 821
 
3.9%
a 816
 
3.9%
р 780
 
3.7%
Other values (47) 11557
55.5%
Uppercase Letter
ValueCountFrequency (%)
Н 44
 
7.9%
П 41
 
7.4%
И 40
 
7.2%
С 40
 
7.2%
О 38
 
6.8%
З 36
 
6.5%
А 28
 
5.0%
Р 27
 
4.9%
М 26
 
4.7%
V 24
 
4.3%
Other values (27) 212
38.1%
Decimal Number
ValueCountFrequency (%)
2 3072
47.8%
0 1303
20.3%
1 667
 
10.4%
5 564
 
8.8%
7 151
 
2.4%
3 141
 
2.2%
6 141
 
2.2%
4 137
 
2.1%
8 136
 
2.1%
9 113
 
1.8%
Other Punctuation
ValueCountFrequency (%)
" 808
45.6%
/ 299
 
16.9%
. 246
 
13.9%
: 184
 
10.4%
, 177
 
10.0%
@ 26
 
1.5%
; 14
 
0.8%
% 12
 
0.7%
& 3
 
0.2%
# 2
 
0.1%
Math Symbol
ValueCountFrequency (%)
< 556
36.6%
> 556
36.6%
= 401
26.4%
+ 8
 
0.5%
Dash Punctuation
ValueCountFrequency (%)
- 947
99.0%
10
 
1.0%
Space Separator
ValueCountFrequency (%)
3505
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 124
100.0%
Other Symbol
ValueCountFrequency (%)
26
100.0%
Open Punctuation
ValueCountFrequency (%)
( 22
100.0%
Close Punctuation
ValueCountFrequency (%)
) 21
100.0%
Final Punctuation
ValueCountFrequency (%)
» 9
100.0%
Initial Punctuation
ValueCountFrequency (%)
« 9
100.0%
Control
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 14391
40.2%
Cyrillic 11727
32.8%
Latin 9662
27.0%

Most frequent character per script

Cyrillic
ValueCountFrequency (%)
я 1156
 
9.9%
а 958
 
8.2%
о 945
 
8.1%
и 821
 
7.0%
р 780
 
6.7%
е 749
 
6.4%
н 739
 
6.3%
л 665
 
5.7%
т 513
 
4.4%
с 472
 
4.0%
Other values (45) 3929
33.5%
Latin
ValueCountFrequency (%)
s 1200
12.4%
e 938
 
9.7%
i 835
 
8.6%
t 827
 
8.6%
a 816
 
8.4%
c 626
 
6.5%
l 552
 
5.7%
n 531
 
5.5%
o 455
 
4.7%
d 414
 
4.3%
Other values (29) 2468
25.5%
Common
ValueCountFrequency (%)
3505
24.4%
2 3072
21.3%
0 1303
 
9.1%
- 947
 
6.6%
" 808
 
5.6%
1 667
 
4.6%
5 564
 
3.9%
< 556
 
3.9%
> 556
 
3.9%
= 401
 
2.8%
Other values (24) 2012
14.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23999
67.1%
Cyrillic 11727
32.8%
Letterlike Symbols 26
 
0.1%
None 18
 
0.1%
Punctuation 10
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3505
 
14.6%
2 3072
 
12.8%
0 1303
 
5.4%
s 1200
 
5.0%
- 947
 
3.9%
e 938
 
3.9%
i 835
 
3.5%
t 827
 
3.4%
a 816
 
3.4%
" 808
 
3.4%
Other values (59) 9748
40.6%
Cyrillic
ValueCountFrequency (%)
я 1156
 
9.9%
а 958
 
8.2%
о 945
 
8.1%
и 821
 
7.0%
р 780
 
6.7%
е 749
 
6.4%
н 739
 
6.3%
л 665
 
5.7%
т 513
 
4.4%
с 472
 
4.0%
Other values (45) 3929
33.5%
Letterlike Symbols
ValueCountFrequency (%)
26
100.0%
Punctuation
ValueCountFrequency (%)
10
100.0%
None
ValueCountFrequency (%)
» 9
50.0%
« 9
50.0%

contact_name
Categorical

Distinct161
Distinct (%)19.4%
Missing1
Missing (%)0.1%
Memory size6.6 KiB
Васильева Наталья Георгиевна
327 
Контактный Центр Банка
105 
Сотрудник ПАО Сбербанк
51 
Коваленко Александр Ан
33 
Самойленко Денис Сергеевич
 
26
Other values (156)
289 

Length

Max length41
Median length37
Mean length25.45367
Min length4

Characters and Unicode

Total characters21152
Distinct characters68
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique107 ?
Unique (%)12.9%

Sample

1st rowКонтактный Центр Банка
2nd rowГаас Александра Георгиевна
3rd rowБрюнель Ева Жисленовна
4th rowПлесняев Александр Александрович
5th rowБеликин Иван Владимирович

Common Values

ValueCountFrequency (%)
Васильева Наталья Георгиевна 327
39.3%
Контактный Центр Банка 105
 
12.6%
Сотрудник ПАО Сбербанк 51
 
6.1%
Коваленко Александр Ан 33
 
4.0%
Самойленко Денис Сергеевич 26
 
3.1%
Шуховцев Алексей Алексеевич 10
 
1.2%
Елтаренко Мария Ивановна 8
 
1.0%
Закржевская Евгения Степановна 8
 
1.0%
Николаев Алексей Андреевич 8
 
1.0%
Остроухова Алла 7
 
0.8%
Other values (151) 248
29.8%

Length

2023-01-29T06:06:13.399355image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
георгиевна 333
 
13.5%
наталья 331
 
13.5%
васильева 328
 
13.3%
контактный 105
 
4.3%
центр 105
 
4.3%
банка 105
 
4.3%
сотрудник 55
 
2.2%
пао 51
 
2.1%
сбербанк 51
 
2.1%
александр 44
 
1.8%
Other values (267) 952
38.7%

Most occurring characters

ValueCountFrequency (%)
а 2800
13.2%
е 1935
 
9.1%
1662
 
7.9%
н 1422
 
6.7%
и 1293
 
6.1%
в 1276
 
6.0%
л 1171
 
5.5%
о 1069
 
5.1%
р 941
 
4.4%
т 826
 
3.9%
Other values (58) 6757
31.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 16919
80.0%
Uppercase Letter 2562
 
12.1%
Space Separator 1662
 
7.9%
Other Punctuation 4
 
< 0.1%
Decimal Number 3
 
< 0.1%
Open Punctuation 1
 
< 0.1%
Close Punctuation 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
а 2800
16.5%
е 1935
11.4%
н 1422
8.4%
и 1293
 
7.6%
в 1276
 
7.5%
л 1171
 
6.9%
о 1069
 
6.3%
р 941
 
5.6%
т 826
 
4.9%
ь 769
 
4.5%
Other values (22) 3417
20.2%
Uppercase Letter
ValueCountFrequency (%)
В 429
16.7%
Н 370
14.4%
Г 359
14.0%
А 265
10.3%
С 222
8.7%
К 185
7.2%
Б 135
 
5.3%
Ц 106
 
4.1%
О 88
 
3.4%
П 73
 
2.8%
Other values (18) 330
12.9%
Decimal Number
ValueCountFrequency (%)
2 1
33.3%
5 1
33.3%
9 1
33.3%
Other Punctuation
ValueCountFrequency (%)
. 3
75.0%
/ 1
 
25.0%
Space Separator
ValueCountFrequency (%)
1662
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Cyrillic 19481
92.1%
Common 1671
 
7.9%

Most frequent character per script

Cyrillic
ValueCountFrequency (%)
а 2800
14.4%
е 1935
 
9.9%
н 1422
 
7.3%
и 1293
 
6.6%
в 1276
 
6.5%
л 1171
 
6.0%
о 1069
 
5.5%
р 941
 
4.8%
т 826
 
4.2%
ь 769
 
3.9%
Other values (50) 5979
30.7%
Common
ValueCountFrequency (%)
1662
99.5%
. 3
 
0.2%
( 1
 
0.1%
2 1
 
0.1%
5 1
 
0.1%
9 1
 
0.1%
) 1
 
0.1%
/ 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
Cyrillic 19481
92.1%
ASCII 1671
 
7.9%

Most frequent character per block

Cyrillic
ValueCountFrequency (%)
а 2800
14.4%
е 1935
 
9.9%
н 1422
 
7.3%
и 1293
 
6.6%
в 1276
 
6.5%
л 1171
 
6.0%
о 1069
 
5.5%
р 941
 
4.8%
т 826
 
4.2%
ь 769
 
3.9%
Other values (50) 5979
30.7%
ASCII
ValueCountFrequency (%)
1662
99.5%
. 3
 
0.2%
( 1
 
0.1%
2 1
 
0.1%
5 1
 
0.1%
9 1
 
0.1%
) 1
 
0.1%
/ 1
 
0.1%

seller_name
Categorical

HIGH CARDINALITY  HIGH CORRELATION 

Distinct83
Distinct (%)10.0%
Missing1
Missing (%)0.1%
Memory size6.6 KiB
АКБ "АК БАРС" БАНК
328 
ПАО Сбербанк
123 
Банк ВТБ (ПАО)
105 
АО "Россельхозбанк"
89 
Физическое лицо
40 
Other values (78)
146 

Length

Max length61
Median length55
Mean length17.904934
Min length9

Characters and Unicode

Total characters14879
Distinct characters68
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique53 ?
Unique (%)6.4%

Sample

1st rowБанк ВТБ (ПАО)
2nd rowФизическое лицо
3rd rowФизическое лицо
4th rowПлесняев Александр Александрович
5th rowБеликин Иван Владимирович

Common Values

ValueCountFrequency (%)
АКБ "АК БАРС" БАНК 328
39.4%
ПАО Сбербанк 123
 
14.8%
Банк ВТБ (ПАО) 105
 
12.6%
АО "Россельхозбанк" 89
 
10.7%
Физическое лицо 40
 
4.8%
Шуховцев Алексей Алексеевич 10
 
1.2%
Закржевская Евгения Степановна 8
 
1.0%
Ульянов Илья Владимирович 7
 
0.8%
Корчашкина Владилена Анатольевна 5
 
0.6%
Казанкова Елена Владимировна 5
 
0.6%
Other values (73) 111
 
13.3%

Length

2023-01-29T06:06:13.460086image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
банк 438
17.1%
акб 329
12.8%
ак 328
12.8%
барс 328
12.8%
пао 232
9.0%
сбербанк 123
 
4.8%
втб 105
 
4.1%
ао 103
 
4.0%
россельхозбанк 89
 
3.5%
лицо 43
 
1.7%
Other values (179) 448
17.5%

Most occurring characters

ValueCountFrequency (%)
А 1764
 
11.9%
1736
 
11.7%
Б 1218
 
8.2%
К 1030
 
6.9%
" 892
 
6.0%
а 607
 
4.1%
е 570
 
3.8%
н 528
 
3.5%
С 502
 
3.4%
к 487
 
3.3%
Other values (58) 5545
37.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 6533
43.9%
Lowercase Letter 5496
36.9%
Space Separator 1736
 
11.7%
Other Punctuation 892
 
6.0%
Close Punctuation 108
 
0.7%
Open Punctuation 108
 
0.7%
Dash Punctuation 2
 
< 0.1%
Initial Punctuation 2
 
< 0.1%
Final Punctuation 2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
а 607
11.0%
е 570
10.4%
н 528
9.6%
к 487
8.9%
о 475
 
8.6%
и 381
 
6.9%
б 353
 
6.4%
с 320
 
5.8%
л 305
 
5.5%
р 269
 
4.9%
Other values (21) 1201
21.9%
Uppercase Letter
ValueCountFrequency (%)
А 1764
27.0%
Б 1218
18.6%
К 1030
15.8%
С 502
 
7.7%
Р 440
 
6.7%
О 400
 
6.1%
Н 377
 
5.8%
П 248
 
3.8%
В 156
 
2.4%
Т 141
 
2.2%
Other values (20) 257
 
3.9%
Space Separator
ValueCountFrequency (%)
1736
100.0%
Other Punctuation
ValueCountFrequency (%)
" 892
100.0%
Close Punctuation
ValueCountFrequency (%)
) 108
100.0%
Open Punctuation
ValueCountFrequency (%)
( 108
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%
Initial Punctuation
ValueCountFrequency (%)
« 2
100.0%
Final Punctuation
ValueCountFrequency (%)
» 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Cyrillic 12029
80.8%
Common 2850
 
19.2%

Most frequent character per script

Cyrillic
ValueCountFrequency (%)
А 1764
14.7%
Б 1218
 
10.1%
К 1030
 
8.6%
а 607
 
5.0%
е 570
 
4.7%
н 528
 
4.4%
С 502
 
4.2%
к 487
 
4.0%
о 475
 
3.9%
Р 440
 
3.7%
Other values (51) 4408
36.6%
Common
ValueCountFrequency (%)
1736
60.9%
" 892
31.3%
) 108
 
3.8%
( 108
 
3.8%
- 2
 
0.1%
« 2
 
0.1%
» 2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
Cyrillic 12029
80.8%
ASCII 2846
 
19.1%
None 4
 
< 0.1%

Most frequent character per block

Cyrillic
ValueCountFrequency (%)
А 1764
14.7%
Б 1218
 
10.1%
К 1030
 
8.6%
а 607
 
5.0%
е 570
 
4.7%
н 528
 
4.4%
С 502
 
4.2%
к 487
 
4.0%
о 475
 
3.9%
Р 440
 
3.7%
Other values (51) 4408
36.6%
ASCII
ValueCountFrequency (%)
1736
61.0%
" 892
31.3%
) 108
 
3.8%
( 108
 
3.8%
- 2
 
0.1%
None
ValueCountFrequency (%)
« 2
50.0%
» 2
50.0%

square
Real number (ℝ)

MISSING  ZEROS 

Distinct269
Distinct (%)62.4%
Missing401
Missing (%)48.2%
Infinite0
Infinite (%)0.0%
Mean541903.37
Minimum0
Maximum45599700
Zeros54
Zeros (%)6.5%
Negative0
Negative (%)0.0%
Memory size6.6 KiB
2023-01-29T06:06:13.516523image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11011
median2438
Q3100000
95-th percentile1184085
Maximum45599700
Range45599700
Interquartile range (IQR)98989

Descriptive statistics

Standard deviation3444529.3
Coefficient of variation (CV)6.3563534
Kurtosis118.22044
Mean541903.37
Median Absolute Deviation (MAD)2438
Skewness10.274396
Sum2.3356035 × 108
Variance1.1864782 × 1013
MonotonicityNot monotonic
2023-01-29T06:06:13.575225image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 54
 
6.5%
100000 31
 
3.7%
1000 14
 
1.7%
1500 10
 
1.2%
2400 9
 
1.1%
1200 8
 
1.0%
1100 5
 
0.6%
2500 4
 
0.5%
10000 4
 
0.5%
60000 3
 
0.4%
Other values (259) 289
34.7%
(Missing) 401
48.2%
ValueCountFrequency (%)
0 54
6.5%
60 1
 
0.1%
150 1
 
0.1%
180.6 1
 
0.1%
188 1
 
0.1%
200 2
 
0.2%
228 1
 
0.1%
250 1
 
0.1%
322 1
 
0.1%
332 1
 
0.1%
ValueCountFrequency (%)
45599700 1
0.1%
41220000 1
0.1%
22000200 1
0.1%
18502500 1
0.1%
14250000 1
0.1%
12414000 1
0.1%
7961200 1
0.1%
6750000 1
0.1%
5500000 1
0.1%
5311300 1
0.1%

price_value
Real number (ℝ)

Distinct463
Distinct (%)55.7%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean17843491
Minimum0
Maximum1.5 × 109
Zeros5
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size6.6 KiB
2023-01-29T06:06:13.634458image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile85500
Q1300000
median337226
Q37540000
95-th percentile40490601
Maximum1.5 × 109
Range1.5 × 109
Interquartile range (IQR)7240000

Descriptive statistics

Standard deviation83758609
Coefficient of variation (CV)4.6940707
Kurtosis143.88902
Mean17843491
Median Absolute Deviation (MAD)235226
Skewness10.438382
Sum1.4827941 × 1010
Variance7.0155046 × 1015
MonotonicityNot monotonic
2023-01-29T06:06:13.698866image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
300000 65
 
7.8%
337219 64
 
7.7%
333000 26
 
3.1%
309000 23
 
2.8%
337209 19
 
2.3%
33723225 15
 
1.8%
92400 15
 
1.8%
102000 13
 
1.6%
354080 13
 
1.6%
139300 11
 
1.3%
Other values (453) 567
68.1%
ValueCountFrequency (%)
0 5
0.6%
8600 1
 
0.1%
15494 1
 
0.1%
19600 1
 
0.1%
20610 1
 
0.1%
21133 1
 
0.1%
25680 1
 
0.1%
30000 1
 
0.1%
31760 1
 
0.1%
32400 1
 
0.1%
ValueCountFrequency (%)
1500000000 1
0.1%
800000000 2
0.2%
573150000 1
0.1%
546560000 1
0.1%
502570000 1
0.1%
500000000 1
0.1%
450000000 1
0.1%
348470000 1
0.1%
345000000 1
0.1%
320000000 1
0.1%

cadastral_geocode
Categorical

HIGH CARDINALITY  MISSING 

Distinct307
Distinct (%)81.0%
Missing453
Missing (%)54.4%
Memory size6.6 KiB
'bool' object is not subscriptable
65 
[55.36827131715471, 49.30005057029149]
 
2
[44.77132659845723, 39.293538656223355]
 
2
[56.17583811643949, 43.64892734998536]
 
2
[56.17880639636869, 43.65770266959024]
 
2
Other values (302)
306 

Length

Max length40
Median length39
Mean length37.794195
Min length34

Characters and Unicode

Total characters14324
Distinct characters30
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique298 ?
Unique (%)78.6%

Sample

1st row[51.94720028246027, 84.88831594503662]
2nd row[56.77190698765615, 38.88962726762723]
3rd row[55.73199800626753, 35.340751735565306]
4th row[55.7008175028533, 37.02131098410721]
5th row[47.34721090904763, 39.57702065464689]

Common Values

ValueCountFrequency (%)
'bool' object is not subscriptable 65
 
7.8%
[55.36827131715471, 49.30005057029149] 2
 
0.2%
[44.77132659845723, 39.293538656223355] 2
 
0.2%
[56.17583811643949, 43.64892734998536] 2
 
0.2%
[56.17880639636869, 43.65770266959024] 2
 
0.2%
[47.51933346121855, 40.807482367991796] 2
 
0.2%
[47.747603480531275, 40.3578462949865] 2
 
0.2%
[50.676454879374035, 36.22393791224141] 2
 
0.2%
[59.45700081882427, 29.631254181475054] 2
 
0.2%
[56.21904254319914, 38.831961664324474] 1
 
0.1%
Other values (297) 297
35.7%
(Missing) 453
54.4%

Length

2023-01-29T06:06:13.758795image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
bool 65
 
6.8%
is 65
 
6.8%
not 65
 
6.8%
subscriptable 65
 
6.8%
object 65
 
6.8%
47.51933346121855 2
 
0.2%
29.631254181475054 2
 
0.2%
36.22393791224141 2
 
0.2%
50.676454879374035 2
 
0.2%
40.3578462949865 2
 
0.2%
Other values (607) 618
64.8%

Most occurring characters

ValueCountFrequency (%)
5 1245
 
8.7%
3 1074
 
7.5%
4 1070
 
7.5%
9 1039
 
7.3%
6 1037
 
7.2%
8 1033
 
7.2%
1 949
 
6.6%
7 945
 
6.6%
2 930
 
6.5%
0 908
 
6.3%
Other values (20) 4094
28.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10230
71.4%
Lowercase Letter 1820
 
12.7%
Other Punctuation 1072
 
7.5%
Space Separator 574
 
4.0%
Open Punctuation 314
 
2.2%
Close Punctuation 314
 
2.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
b 260
14.3%
o 260
14.3%
s 195
10.7%
t 195
10.7%
i 130
7.1%
c 130
7.1%
e 130
7.1%
l 130
7.1%
p 65
 
3.6%
r 65
 
3.6%
Other values (4) 260
14.3%
Decimal Number
ValueCountFrequency (%)
5 1245
12.2%
3 1074
10.5%
4 1070
10.5%
9 1039
10.2%
6 1037
10.1%
8 1033
10.1%
1 949
9.3%
7 945
9.2%
2 930
9.1%
0 908
8.9%
Other Punctuation
ValueCountFrequency (%)
. 628
58.6%
, 314
29.3%
' 130
 
12.1%
Space Separator
ValueCountFrequency (%)
574
100.0%
Open Punctuation
ValueCountFrequency (%)
[ 314
100.0%
Close Punctuation
ValueCountFrequency (%)
] 314
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 12504
87.3%
Latin 1820
 
12.7%

Most frequent character per script

Common
ValueCountFrequency (%)
5 1245
10.0%
3 1074
8.6%
4 1070
8.6%
9 1039
8.3%
6 1037
8.3%
8 1033
8.3%
1 949
7.6%
7 945
7.6%
2 930
7.4%
0 908
 
7.3%
Other values (6) 2274
18.2%
Latin
ValueCountFrequency (%)
b 260
14.3%
o 260
14.3%
s 195
10.7%
t 195
10.7%
i 130
7.1%
c 130
7.1%
e 130
7.1%
l 130
7.1%
p 65
 
3.6%
r 65
 
3.6%
Other values (4) 260
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14324
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 1245
 
8.7%
3 1074
 
7.5%
4 1070
 
7.5%
9 1039
 
7.3%
6 1037
 
7.2%
8 1033
 
7.2%
1 949
 
6.6%
7 945
 
6.6%
2 930
 
6.5%
0 908
 
6.3%
Other values (20) 4094
28.6%

region_geocode
Categorical

HIGH CARDINALITY  MISSING 

Distinct243
Distinct (%)29.9%
Missing19
Missing (%)2.3%
Memory size6.6 KiB
[55.4559723, 49.43731193661616]
322 
[54.5684698, 21.121404102960874]
69 
[56.1345574, 38.85192758087219]
 
31
[59.83267395, 29.503088298145748]
 
11
[47.370534899999996, 39.46597632166487]
 
10
Other values (238)
370 

Length

Max length40
Median length39
Mean length30.613776
Min length21

Characters and Unicode

Total characters24889
Distinct characters15
Distinct categories5 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique166 ?
Unique (%)20.4%

Sample

1st row[55.6711507, 37.2727963]
2nd row[51.9931851, 84.9819571]
3rd row[56.80208355, 38.64899133747492]
4th row[55.506478, 36.0213092]
5th row[55.6711507, 37.2727963]

Common Values

ValueCountFrequency (%)
[55.4559723, 49.43731193661616] 322
38.7%
[54.5684698, 21.121404102960874] 69
 
8.3%
[56.1345574, 38.85192758087219] 31
 
3.7%
[59.83267395, 29.503088298145748] 11
 
1.3%
[47.370534899999996, 39.46597632166487] 10
 
1.2%
[57.9863299, 56.2881011] 7
 
0.8%
[55.039115949999996, 43.242821649999996] 6
 
0.7%
[44.6342653, 39.1363613] 6
 
0.7%
[56.132591399999995, 38.43552736326269] 6
 
0.7%
[56.14980385, 47.20802108704281] 6
 
0.7%
Other values (233) 339
40.7%
(Missing) 19
 
2.3%

Length

2023-01-29T06:06:13.811562image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
55.4559723 322
19.8%
49.43731193661616 322
19.8%
54.5684698 69
 
4.2%
21.121404102960874 69
 
4.2%
56.1345574 31
 
1.9%
38.85192758087219 31
 
1.9%
59.83267395 11
 
0.7%
29.503088298145748 11
 
0.7%
47.370534899999996 10
 
0.6%
39.46597632166487 10
 
0.6%
Other values (476) 740
45.5%

Most occurring characters

ValueCountFrequency (%)
5 2899
11.6%
1 2464
9.9%
6 2373
9.5%
3 2319
9.3%
4 2303
9.3%
9 2195
8.8%
7 1633
 
6.6%
. 1626
 
6.5%
2 1442
 
5.8%
8 1199
 
4.8%
Other values (5) 4436
17.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20011
80.4%
Other Punctuation 2439
 
9.8%
Open Punctuation 813
 
3.3%
Space Separator 813
 
3.3%
Close Punctuation 813
 
3.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 2899
14.5%
1 2464
12.3%
6 2373
11.9%
3 2319
11.6%
4 2303
11.5%
9 2195
11.0%
7 1633
8.2%
2 1442
7.2%
8 1199
6.0%
0 1184
5.9%
Other Punctuation
ValueCountFrequency (%)
. 1626
66.7%
, 813
33.3%
Open Punctuation
ValueCountFrequency (%)
[ 813
100.0%
Space Separator
ValueCountFrequency (%)
813
100.0%
Close Punctuation
ValueCountFrequency (%)
] 813
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 24889
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 2899
11.6%
1 2464
9.9%
6 2373
9.5%
3 2319
9.3%
4 2303
9.3%
9 2195
8.8%
7 1633
 
6.6%
. 1626
 
6.5%
2 1442
 
5.8%
8 1199
 
4.8%
Other values (5) 4436
17.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 24889
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 2899
11.6%
1 2464
9.9%
6 2373
9.5%
3 2319
9.3%
4 2303
9.3%
9 2195
8.8%
7 1633
 
6.6%
. 1626
 
6.5%
2 1442
 
5.8%
8 1199
 
4.8%
Other values (5) 4436
17.8%

shown_by_days
Real number (ℝ)

Distinct825
Distinct (%)100.0%
Missing7
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean252.8238
Minimum8.8844074
Maximum1611.8844
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.6 KiB
2023-01-29T06:06:13.870444image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum8.8844074
5-th percentile35.284407
Q1110.88441
median177.88441
Q3280.88441
95-th percentile633.08441
Maximum1611.8844
Range1603
Interquartile range (IQR)170

Descriptive statistics

Standard deviation251.90603
Coefficient of variation (CV)0.99636991
Kurtosis8.3955244
Mean252.8238
Median Absolute Deviation (MAD)88
Skewness2.5960485
Sum208579.64
Variance63456.647
MonotonicityNot monotonic
2023-01-29T06:06:13.930488image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
177.884408 1
 
0.1%
177.884408 1
 
0.1%
177.884408 1
 
0.1%
177.884408 1
 
0.1%
177.884408 1
 
0.1%
177.884408 1
 
0.1%
177.884408 1
 
0.1%
177.884408 1
 
0.1%
177.884408 1
 
0.1%
177.884408 1
 
0.1%
Other values (815) 815
98.0%
(Missing) 7
 
0.8%
ValueCountFrequency (%)
8.88440735 1
0.1%
9.884407352 1
0.1%
9.884407354 1
0.1%
9.884407356 1
0.1%
9.884407358 1
0.1%
18.88440736 1
0.1%
18.88440736 1
0.1%
19.88440736 1
0.1%
19.88440737 1
0.1%
19.88440737 1
0.1%
ValueCountFrequency (%)
1611.884407 1
0.1%
1574.884407 1
0.1%
1504.884407 1
0.1%
1504.884407 1
0.1%
1504.884407 1
0.1%
1463.884407 1
0.1%
1456.884407 1
0.1%
1427.884407 1
0.1%
1421.884407 1
0.1%
1405.884407 1
0.1%

distance
Real number (ℝ)

Distinct296
Distinct (%)97.4%
Missing528
Missing (%)63.5%
Infinite0
Infinite (%)0.0%
Mean33.163109
Minimum0.48603435
Maximum1692.3069
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.6 KiB
2023-01-29T06:06:13.993597image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.48603435
5-th percentile2.8593684
Q19.4811332
median13.019486
Q323.100394
95-th percentile127.44984
Maximum1692.3069
Range1691.8208
Interquartile range (IQR)13.619261

Descriptive statistics

Standard deviation115.13672
Coefficient of variation (CV)3.4718311
Kurtosis153.31594
Mean33.163109
Median Absolute Deviation (MAD)5.7077596
Skewness11.532099
Sum10081.585
Variance13256.463
MonotonicityNot monotonic
2023-01-29T06:06:14.051513image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19.67632961 2
 
0.2%
12.03593917 2
 
0.2%
6.200741874 2
 
0.2%
129.5522787 2
 
0.2%
13.07334263 2
 
0.2%
129.1180574 2
 
0.2%
0.4860343487 2
 
0.2%
19.62605754 2
 
0.2%
5.047385417 1
 
0.1%
26.37520857 1
 
0.1%
Other values (286) 286
34.4%
(Missing) 528
63.5%
ValueCountFrequency (%)
0.4860343487 2
0.2%
0.6212485317 1
0.1%
0.7594781045 1
0.1%
0.9717877712 1
0.1%
1.401710286 1
0.1%
1.493291323 1
0.1%
1.525725146 1
0.1%
1.664372679 1
0.1%
1.782009199 1
0.1%
2.069534288 1
0.1%
ValueCountFrequency (%)
1692.306852 1
0.1%
898.447501 1
0.1%
380.62581 1
0.1%
310.4231073 1
0.1%
218.8674909 1
0.1%
164.4198387 1
0.1%
164.384646 1
0.1%
164.3583677 1
0.1%
138.2549015 1
0.1%
135.6516675 1
0.1%

region_square
Real number (ℝ)

Distinct66
Distinct (%)8.0%
Missing7
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean118104.02
Minimum1403
Maximum3083523
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.6 KiB
2023-01-29T06:06:14.113987image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1403
5-th percentile15125
Q144329
median67847
Q376624
95-th percentile194307
Maximum3083523
Range3082120
Interquartile range (IQR)32295

Descriptive statistics

Standard deviation290110.1
Coefficient of variation (CV)2.4563948
Kurtosis51.747968
Mean118104.02
Median Absolute Deviation (MAD)16061
Skewness6.8841271
Sum97435819
Variance8.4163873 × 1010
MonotonicityNot monotonic
2023-01-29T06:06:14.172503image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
67847 331
39.8%
15125 74
 
8.9%
29084 40
 
4.8%
44329 31
 
3.7%
100967 30
 
3.6%
83908 28
 
3.4%
75485 21
 
2.5%
36177 15
 
1.8%
84201 15
 
1.8%
160236 13
 
1.6%
Other values (56) 227
27.3%
ValueCountFrequency (%)
1403 4
 
0.5%
2561 2
 
0.2%
3123 2
 
0.2%
7792 1
 
0.1%
7987 1
 
0.1%
12470 2
 
0.2%
15125 74
8.9%
18343 7
 
0.8%
21437 1
 
0.1%
23375 1
 
0.1%
ValueCountFrequency (%)
3083523 1
 
0.1%
2366797 9
1.1%
1464173 2
 
0.2%
787633 8
1.0%
774846 8
1.0%
589913 2
 
0.2%
431892 5
0.6%
361908 1
 
0.1%
351334 4
0.5%
314391 1
 
0.1%

population
Real number (ℝ)

Distinct66
Distinct (%)8.0%
Missing7
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean3109690.7
Minimum210808
Maximum13015126
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.6 KiB
2023-01-29T06:06:14.233499image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum210808
5-th percentile977951
Q11342235
median4000084
Q34000084
95-th percentile5832042
Maximum13015126
Range12804318
Interquartile range (IQR)2657849

Descriptive statistics

Standard deviation1827409.2
Coefficient of variation (CV)0.5876498
Kurtosis2.7510986
Mean3109690.7
Median Absolute Deviation (MAD)1143758
Skewness1.0776528
Sum2.5654949 × 109
Variance3.3394242 × 1012
MonotonicityNot monotonic
2023-01-29T06:06:14.299237image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4000084 331
39.8%
1030979 74
 
8.9%
1342235 40
 
4.8%
8542257 31
 
3.7%
4192322 30
 
3.6%
2006022 28
 
3.4%
5832042 21
 
2.5%
1205637 15
 
1.8%
1226038 15
 
1.8%
2525149 13
 
1.6%
Other values (56) 227
27.3%
ValueCountFrequency (%)
210808 1
 
0.1%
498285 1
 
0.1%
511316 2
 
0.2%
532384 2
 
0.2%
577996 3
 
0.4%
581578 10
1.2%
596899 4
 
0.5%
676351 1
 
0.1%
685393 1
 
0.1%
763570 1
 
0.1%
ValueCountFrequency (%)
13015126 2
 
0.2%
8542257 31
 
3.7%
5832042 21
 
2.5%
5607916 4
 
0.5%
4263691 8
 
1.0%
4192322 30
 
3.6%
4091621 8
 
1.0%
4000084 331
39.8%
3827679 2
 
0.2%
3421556 5
 
0.6%

region_capital
Categorical

HIGH CARDINALITY  HIGH CORRELATION 

Distinct64
Distinct (%)7.8%
Missing7
Missing (%)0.8%
Memory size6.6 KiB
Казань
331 
Калининград
74 
Владимир
40 
Москва
 
33
Санкт-Петербург
 
32
Other values (59)
315 

Length

Max length16
Median length15
Mean length7.8981818
Min length3

Characters and Unicode

Total characters6516
Distinct characters54
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)1.3%

Sample

1st rowМосква
2nd rowБарнаул
3rd rowЯрославль
4th rowМосква
5th rowМосква

Common Values

ValueCountFrequency (%)
Казань 331
39.8%
Калининград 74
 
8.9%
Владимир 40
 
4.8%
Москва 33
 
4.0%
Санкт-Петербург 32
 
3.8%
Ростов-на-Дону 30
 
3.6%
Краснодар 21
 
2.5%
Тверь 15
 
1.8%
Ярославль 15
 
1.8%
Пермь 13
 
1.6%
Other values (54) 221
26.6%

Length

2023-01-29T06:06:14.357558image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
казань 331
39.1%
калининград 74
 
8.7%
владимир 40
 
4.7%
москва 33
 
3.9%
санкт-петербург 32
 
3.8%
ростов-на-дону 30
 
3.5%
новгород 21
 
2.5%
краснодар 21
 
2.5%
ярославль 15
 
1.8%
тверь 15
 
1.8%
Other values (55) 234
27.7%

Most occurring characters

ValueCountFrequency (%)
а 1188
18.2%
н 676
 
10.4%
К 459
 
7.0%
р 428
 
6.6%
ь 392
 
6.0%
о 358
 
5.5%
з 340
 
5.2%
и 315
 
4.8%
л 214
 
3.3%
с 199
 
3.1%
Other values (44) 1947
29.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5483
84.1%
Uppercase Letter 914
 
14.0%
Dash Punctuation 98
 
1.5%
Space Separator 21
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
а 1188
21.7%
н 676
12.3%
р 428
 
7.8%
ь 392
 
7.1%
о 358
 
6.5%
з 340
 
6.2%
и 315
 
5.7%
л 214
 
3.9%
с 199
 
3.6%
д 181
 
3.3%
Other values (21) 1192
21.7%
Uppercase Letter
ValueCountFrequency (%)
К 459
50.2%
В 68
 
7.4%
П 53
 
5.8%
С 51
 
5.6%
Н 41
 
4.5%
М 39
 
4.3%
Р 34
 
3.7%
Д 30
 
3.3%
Т 22
 
2.4%
У 17
 
1.9%
Other values (11) 100
 
10.9%
Dash Punctuation
ValueCountFrequency (%)
- 98
100.0%
Space Separator
ValueCountFrequency (%)
21
100.0%

Most occurring scripts

ValueCountFrequency (%)
Cyrillic 6397
98.2%
Common 119
 
1.8%

Most frequent character per script

Cyrillic
ValueCountFrequency (%)
а 1188
18.6%
н 676
 
10.6%
К 459
 
7.2%
р 428
 
6.7%
ь 392
 
6.1%
о 358
 
5.6%
з 340
 
5.3%
и 315
 
4.9%
л 214
 
3.3%
с 199
 
3.1%
Other values (42) 1828
28.6%
Common
ValueCountFrequency (%)
- 98
82.4%
21
 
17.6%

Most occurring blocks

ValueCountFrequency (%)
Cyrillic 6397
98.2%
ASCII 119
 
1.8%

Most frequent character per block

Cyrillic
ValueCountFrequency (%)
а 1188
18.6%
н 676
 
10.6%
К 459
 
7.2%
р 428
 
6.7%
ь 392
 
6.1%
о 358
 
5.6%
з 340
 
5.3%
и 315
 
4.9%
л 214
 
3.3%
с 199
 
3.1%
Other values (42) 1828
28.6%
ASCII
ValueCountFrequency (%)
- 98
82.4%
21
 
17.6%

grp
Real number (ℝ)

Distinct66
Distinct (%)8.0%
Missing7
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean1695.0823
Minimum50.6
Maximum17881.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.6 KiB
2023-01-29T06:06:14.412966image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum50.6
5-th percentile298.46
Q1514
median1673.7
Q32469.2
95-th percentile2469.2
Maximum17881.5
Range17830.9
Interquartile range (IQR)1955.2

Descriptive statistics

Standard deviation1311.2424
Coefficient of variation (CV)0.77355678
Kurtosis54.681661
Mean1695.0823
Median Absolute Deviation (MAD)795.5
Skewness4.655951
Sum1398442.9
Variance1719356.6
MonotonicityNot monotonic
2023-01-29T06:06:14.554325image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2469.2 331
39.8%
460.9 74
 
8.9%
440.5 40
 
4.8%
4201.8 31
 
3.7%
1446.2 30
 
3.6%
1104.4 28
 
3.4%
2344.6 21
 
2.5%
560.6 15
 
1.8%
441.7 15
 
1.8%
1318.5 13
 
1.6%
Other values (56) 227
27.3%
ValueCountFrequency (%)
50.6 1
 
0.1%
55.5 2
0.2%
108.4 1
 
0.1%
130 1
 
0.1%
145.7 2
0.2%
164.2 4
0.5%
177.7 1
 
0.1%
180.3 3
0.4%
197.8 1
 
0.1%
213 3
0.4%
ValueCountFrequency (%)
17881.5 2
 
0.2%
4201.8 31
 
3.7%
4193.5 4
 
0.5%
2469.2 331
39.8%
2344.6 21
 
2.5%
2280 9
 
1.1%
2277.6 8
 
1.0%
1673.7 8
 
1.0%
1510.5 8
 
1.0%
1473.7 5
 
0.6%

criminality
Real number (ℝ)

Distinct62
Distinct (%)7.5%
Missing7
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean102.752
Minimum32.9
Maximum161.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.6 KiB
2023-01-29T06:06:14.613025image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum32.9
5-th percentile72.9
Q193.4
median102.1
Q3104.8
95-th percentile130.74
Maximum161.8
Range128.9
Interquartile range (IQR)11.4

Descriptive statistics

Standard deviation15.730045
Coefficient of variation (CV)0.15308748
Kurtosis3.3249776
Mean102.752
Median Absolute Deviation (MAD)5.3
Skewness0.22778776
Sum84770.4
Variance247.43432
MonotonicityNot monotonic
2023-01-29T06:06:14.672992image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
102.1 331
39.8%
93.4 74
 
8.9%
91.1 42
 
5.0%
112.4 33
 
4.0%
72.9 31
 
3.7%
104.8 30
 
3.6%
104.6 21
 
2.5%
130.5 18
 
2.2%
105.1 15
 
1.8%
122.5 13
 
1.6%
Other values (52) 217
26.1%
ValueCountFrequency (%)
32.9 2
 
0.2%
36.6 3
 
0.4%
64.6 4
 
0.5%
67.9 2
 
0.2%
71.8 6
 
0.7%
72.2 1
 
0.1%
72.9 31
3.7%
77.4 1
 
0.1%
80.4 7
 
0.8%
81 2
 
0.2%
ValueCountFrequency (%)
161.8 1
 
0.1%
161.6 1
 
0.1%
156 4
 
0.5%
155.3 5
0.6%
150.1 2
 
0.2%
141.2 5
0.6%
138 5
0.6%
137.4 3
 
0.4%
135.9 10
1.2%
133.2 1
 
0.1%

km_to_msk
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct422
Distinct (%)51.3%
Missing9
Missing (%)1.1%
Infinite0
Infinite (%)0.0%
Mean975.5457
Minimum16.87947
Maximum6497.4935
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.6 KiB
2023-01-29T06:06:14.732852image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum16.87947
5-th percentile91.305767
Q1659.08386
median744.92311
Q31056.9539
95-th percentile3094.5682
Maximum6497.4935
Range6480.614
Interquartile range (IQR)397.87003

Descriptive statistics

Standard deviation991.44026
Coefficient of variation (CV)1.016293
Kurtosis12.569647
Mean975.5457
Median Absolute Deviation (MAD)198.84152
Skewness3.32487
Sum802874.11
Variance982953.79
MonotonicityNot monotonic
2023-01-29T06:06:14.790100image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
744.9231092 291
35.0%
1056.953892 69
 
8.3%
1161.222751 5
 
0.6%
1242.47594 4
 
0.5%
841.3278166 4
 
0.5%
491.2554586 3
 
0.4%
87.68467906 3
 
0.4%
233.7515941 3
 
0.4%
65.78062502 3
 
0.4%
334.2451965 3
 
0.4%
Other values (412) 435
52.3%
(Missing) 9
 
1.1%
ValueCountFrequency (%)
16.8794703 1
0.1%
21.03943576 1
0.1%
23.37079494 1
0.1%
23.78592371 1
0.1%
24.5393449 1
0.1%
24.57874628 1
0.1%
26.57958118 1
0.1%
26.60744935 1
0.1%
26.63285027 1
0.1%
26.79624953 1
0.1%
ValueCountFrequency (%)
6497.493486 1
0.1%
6489.494075 1
0.1%
6438.046318 1
0.1%
6360.736061 1
0.1%
6166.224962 1
0.1%
6161.725871 1
0.1%
6161.705779 1
0.1%
6159.977662 1
0.1%
6085.568761 1
0.1%
6001.942179 1
0.1%

region_capital_geocode
Categorical

HIGH CARDINALITY  HIGH CORRELATION 

Distinct64
Distinct (%)7.8%
Missing7
Missing (%)0.8%
Memory size6.6 KiB
[55.7823547, 49.1242266]
331 
[54.710128, 20.5105838]
74 
[56.1288899, 40.4075203]
40 
[55.7504461, 37.6174943]
 
33
[59.938732, 30.316229]
 
32
Other values (59)
315 

Length

Max length40
Median length24
Mean length24.139394
Min length21

Characters and Unicode

Total characters19915
Distinct characters15
Distinct categories5 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)1.3%

Sample

1st row[55.7504461, 37.6174943]
2nd row[53.347402, 83.7784496]
3rd row[57.6263877, 39.8933705]
4th row[55.7504461, 37.6174943]
5th row[55.7504461, 37.6174943]

Common Values

ValueCountFrequency (%)
[55.7823547, 49.1242266] 331
39.8%
[54.710128, 20.5105838] 74
 
8.9%
[56.1288899, 40.4075203] 40
 
4.8%
[55.7504461, 37.6174943] 33
 
4.0%
[59.938732, 30.316229] 32
 
3.8%
[47.2216548, 39.7096061] 30
 
3.6%
[45.7684014, 39.0261044] 21
 
2.5%
[56.8596713, 35.89524161906262] 15
 
1.8%
[57.6263877, 39.8933705] 15
 
1.8%
[58.02148705, 56.23076652679421] 13
 
1.6%
Other values (54) 221
26.6%

Length

2023-01-29T06:06:14.847740image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
55.7823547 331
20.1%
49.1242266 331
20.1%
54.710128 74
 
4.5%
20.5105838 74
 
4.5%
56.1288899 40
 
2.4%
40.4075203 40
 
2.4%
55.7504461 33
 
2.0%
37.6174943 33
 
2.0%
59.938732 32
 
1.9%
30.316229 32
 
1.9%
Other values (118) 630
38.2%

Most occurring characters

ValueCountFrequency (%)
5 2243
11.3%
2 2159
10.8%
4 1904
9.6%
. 1650
 
8.3%
7 1472
 
7.4%
6 1458
 
7.3%
1 1259
 
6.3%
8 1231
 
6.2%
3 1178
 
5.9%
9 1106
 
5.6%
Other values (5) 4255
21.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14965
75.1%
Other Punctuation 2475
 
12.4%
Open Punctuation 825
 
4.1%
Space Separator 825
 
4.1%
Close Punctuation 825
 
4.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 2243
15.0%
2 2159
14.4%
4 1904
12.7%
7 1472
9.8%
6 1458
9.7%
1 1259
8.4%
8 1231
8.2%
3 1178
7.9%
9 1106
7.4%
0 955
6.4%
Other Punctuation
ValueCountFrequency (%)
. 1650
66.7%
, 825
33.3%
Open Punctuation
ValueCountFrequency (%)
[ 825
100.0%
Space Separator
ValueCountFrequency (%)
825
100.0%
Close Punctuation
ValueCountFrequency (%)
] 825
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 19915
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 2243
11.3%
2 2159
10.8%
4 1904
9.6%
. 1650
 
8.3%
7 1472
 
7.4%
6 1458
 
7.3%
1 1259
 
6.3%
8 1231
 
6.2%
3 1178
 
5.9%
9 1106
 
5.6%
Other values (5) 4255
21.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 19915
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 2243
11.3%
2 2159
10.8%
4 1904
9.6%
. 1650
 
8.3%
7 1472
 
7.4%
6 1458
 
7.3%
1 1259
 
6.3%
8 1231
 
6.2%
3 1178
 
5.9%
9 1106
 
5.6%
Other values (5) 4255
21.4%

km_to_region_capital
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct305
Distinct (%)97.4%
Missing519
Missing (%)62.4%
Infinite0
Infinite (%)0.0%
Mean77.565828
Minimum0.65027232
Maximum898.4475
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.6 KiB
2023-01-29T06:06:14.904559image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.65027232
5-th percentile6.9217746
Q127.048615
median47.436639
Q398.536464
95-th percentile224.07547
Maximum898.4475
Range897.79723
Interquartile range (IQR)71.487849

Descriptive statistics

Standard deviation87.521416
Coefficient of variation (CV)1.1283502
Kurtosis26.884932
Mean77.565828
Median Absolute Deviation (MAD)30.286965
Skewness3.9498949
Sum24278.104
Variance7659.9983
MonotonicityNot monotonic
2023-01-29T06:06:14.960573image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
27.23842697 2
 
0.2%
47.41670721 2
 
0.2%
112.7816602 2
 
0.2%
27.72919993 2
 
0.2%
27.09554432 2
 
0.2%
89.28061632 2
 
0.2%
76.19750071 2
 
0.2%
66.08931599 2
 
0.2%
9.4991183 1
 
0.1%
3.319434816 1
 
0.1%
Other values (295) 295
35.5%
(Missing) 519
62.4%
ValueCountFrequency (%)
0.6502723172 1
0.1%
1.641069482 1
0.1%
1.801998597 1
0.1%
2.378020515 1
0.1%
2.48906318 1
0.1%
2.574316958 1
0.1%
2.628375811 1
0.1%
3.115415339 1
0.1%
3.319434816 1
0.1%
3.462921479 1
0.1%
ValueCountFrequency (%)
898.447501 1
0.1%
449.5720555 1
0.1%
385.3477927 1
0.1%
384.252524 1
0.1%
383.6162764 1
0.1%
380.7672295 1
0.1%
378.4707918 1
0.1%
377.1120592 1
0.1%
288.5967788 1
0.1%
279.5505 1
0.1%

cadastral_lat
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct306
Distinct (%)97.5%
Missing518
Missing (%)62.3%
Infinite0
Infinite (%)0.0%
Mean54.27383
Minimum42.986816
Maximum62.338963
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.6 KiB
2023-01-29T06:06:15.018201image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum42.986816
5-th percentile44.771327
Q152.434057
median55.369562
Q356.237393
95-th percentile59.713652
Maximum62.338963
Range19.352147
Interquartile range (IQR)3.8033359

Descriptive statistics

Standard deviation4.1554821
Coefficient of variation (CV)0.076565116
Kurtosis0.57540331
Mean54.27383
Median Absolute Deviation (MAD)1.3590062
Skewness-1.1186942
Sum17041.983
Variance17.268032
MonotonicityNot monotonic
2023-01-29T06:06:15.075925image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50.67645488 2
 
0.2%
55.36827132 2
 
0.2%
44.7713266 2
 
0.2%
56.17583812 2
 
0.2%
56.1788064 2
 
0.2%
47.51933346 2
 
0.2%
47.74760348 2
 
0.2%
59.45700082 2
 
0.2%
47.26573931 1
 
0.1%
56.83200364 1
 
0.1%
Other values (296) 296
35.6%
(Missing) 518
62.3%
ValueCountFrequency (%)
42.98681634 1
0.1%
43.01658916 1
0.1%
43.11057466 1
0.1%
43.26280939 1
0.1%
43.52513985 1
0.1%
43.57739023 1
0.1%
43.66082499 1
0.1%
43.73050108 1
0.1%
43.73067934 1
0.1%
43.9603872 1
0.1%
ValueCountFrequency (%)
62.33896296 1
0.1%
60.7628398 1
0.1%
60.16258588 1
0.1%
60.01191817 1
0.1%
59.90218258 1
0.1%
59.89186599 1
0.1%
59.8917977 1
0.1%
59.89173744 1
0.1%
59.89167711 1
0.1%
59.89133497 1
0.1%

cadastral_long
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct306
Distinct (%)97.5%
Missing518
Missing (%)62.3%
Infinite0
Infinite (%)0.0%
Mean49.278708
Minimum19.878239
Maximum140.2393
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.6 KiB
2023-01-29T06:06:15.135979image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum19.878239
5-th percentile29.697796
Q137.919608
median40.314743
Q349.690014
95-th percentile100.9796
Maximum140.2393
Range120.36106
Interquartile range (IQR)11.770406

Descriptive statistics

Standard deviation22.476024
Coefficient of variation (CV)0.45610009
Kurtosis4.6407113
Mean49.278708
Median Absolute Deviation (MAD)7.0438159
Skewness2.1509626
Sum15473.514
Variance505.17164
MonotonicityNot monotonic
2023-01-29T06:06:15.195637image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36.22393791 2
 
0.2%
49.30005057 2
 
0.2%
39.29353866 2
 
0.2%
43.64892735 2
 
0.2%
43.65770267 2
 
0.2%
40.80748237 2
 
0.2%
40.35784629 2
 
0.2%
29.63125418 2
 
0.2%
39.60212825 1
 
0.1%
53.21359058 1
 
0.1%
Other values (296) 296
35.6%
(Missing) 518
62.3%
ValueCountFrequency (%)
19.87823869 1
0.1%
20.41437572 1
0.1%
20.46784496 1
0.1%
20.46936551 1
0.1%
21.81298283 1
0.1%
28.4388781 1
0.1%
28.44723639 1
0.1%
28.57691105 1
0.1%
28.63303848 1
0.1%
29.14550822 1
0.1%
ValueCountFrequency (%)
140.2393009 1
0.1%
136.4751183 1
0.1%
135.3765884 1
0.1%
134.8814007 1
0.1%
134.8812984 1
0.1%
132.8698214 1
0.1%
131.9579886 1
0.1%
129.6753818 1
0.1%
117.3070405 1
0.1%
113.5062224 1
0.1%

region_lat
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct243
Distinct (%)29.9%
Missing19
Missing (%)2.3%
Infinite0
Infinite (%)0.0%
Mean54.651132
Minimum42.607397
Maximum64.421817
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.6 KiB
2023-01-29T06:06:15.256345image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum42.607397
5-th percentile46.723453
Q154.56847
median55.455972
Q355.56411
95-th percentile59.099998
Maximum64.421817
Range21.81442
Interquartile range (IQR)0.99564005

Descriptive statistics

Standard deviation3.3277998
Coefficient of variation (CV)0.060891691
Kurtosis3.3569155
Mean54.651132
Median Absolute Deviation (MAD)0.6785851
Skewness-1.6192722
Sum44431.37
Variance11.074252
MonotonicityNot monotonic
2023-01-29T06:06:15.313606image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
55.4559723 322
38.7%
54.5684698 69
 
8.3%
56.1345574 31
 
3.7%
59.83267395 11
 
1.3%
47.3705349 10
 
1.2%
57.9863299 7
 
0.8%
55.03911595 6
 
0.7%
44.6342653 6
 
0.7%
56.1325914 6
 
0.7%
56.14980385 6
 
0.7%
Other values (233) 339
40.7%
(Missing) 19
 
2.3%
ValueCountFrequency (%)
42.6073975 1
0.1%
42.84762685 1
0.1%
42.9709257 1
0.1%
43.1214508 1
0.1%
43.1841908 1
0.1%
43.1949582 1
0.1%
43.246916 1
0.1%
43.2536601 1
0.1%
43.4695247 2
0.2%
43.50532535 1
0.1%
ValueCountFrequency (%)
64.4218171 1
 
0.1%
63.81629865 1
 
0.1%
62.4035707 1
 
0.1%
61.681946 1
 
0.1%
61.48327 1
 
0.1%
60.8005516 2
0.2%
60.7254639 2
0.2%
60.3572197 1
 
0.1%
60.0981084 1
 
0.1%
59.938732 3
0.4%

region_long
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct243
Distinct (%)29.9%
Missing19
Missing (%)2.3%
Infinite0
Infinite (%)0.0%
Mean47.292734
Minimum20.564456
Maximum140.17587
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.6 KiB
2023-01-29T06:06:15.370965image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum20.564456
5-th percentile21.121404
Q138.786651
median49.437312
Q349.437312
95-th percentile86.123979
Maximum140.17587
Range119.61141
Interquartile range (IQR)10.65066

Descriptive statistics

Standard deviation18.840922
Coefficient of variation (CV)0.39838936
Kurtosis7.1561107
Mean47.292734
Median Absolute Deviation (MAD)6.8810185
Skewness2.1819214
Sum38448.993
Variance354.98035
MonotonicityNot monotonic
2023-01-29T06:06:15.430346image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
49.43731194 322
38.7%
21.1214041 69
 
8.3%
38.85192758 31
 
3.7%
29.5030883 11
 
1.3%
39.46597632 10
 
1.2%
56.2881011 7
 
0.8%
43.24282165 6
 
0.7%
39.1363613 6
 
0.7%
38.43552736 6
 
0.7%
47.20802109 6
 
0.7%
Other values (233) 339
40.7%
(Missing) 19
 
2.3%
ValueCountFrequency (%)
20.56445605 1
 
0.1%
20.6335525 1
 
0.1%
20.70309398 2
 
0.2%
21.1214041 69
8.3%
21.78338098 1
 
0.1%
25.4856617 1
 
0.1%
27.96862994 1
 
0.1%
28.68977966 1
 
0.1%
28.71433175 1
 
0.1%
28.737268 2
 
0.2%
ValueCountFrequency (%)
140.1758697 1
 
0.1%
137.020782 1
 
0.1%
136.48938 1
 
0.1%
135.1829132 1
 
0.1%
133.7672189 4
0.5%
133.0573025 1
 
0.1%
133.0279934 1
 
0.1%
131.8893427 1
 
0.1%
129.1540617 1
 
0.1%
115.3040516 1
 
0.1%

region_capital_lat
Real number (ℝ)

Distinct64
Distinct (%)7.8%
Missing7
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean54.864319
Minimum42.983024
Maximum64.543022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.6 KiB
2023-01-29T06:06:15.491602image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum42.983024
5-th percentile47.221655
Q154.710128
median55.782355
Q355.782355
95-th percentile59.218876
Maximum64.543022
Range21.559998
Interquartile range (IQR)1.0722267

Descriptive statistics

Standard deviation3.2954439
Coefficient of variation (CV)0.060065339
Kurtosis3.0843968
Mean54.864319
Median Absolute Deviation (MAD)0.3465352
Skewness-1.6087292
Sum45263.063
Variance10.859951
MonotonicityNot monotonic
2023-01-29T06:06:15.548165image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
55.7823547 331
39.8%
54.710128 74
 
8.9%
56.1288899 40
 
4.8%
55.7504461 33
 
4.0%
59.938732 32
 
3.8%
47.2216548 30
 
3.6%
45.7684014 21
 
2.5%
56.8596713 15
 
1.8%
57.6263877 15
 
1.8%
58.02148705 13
 
1.6%
Other values (54) 221
26.6%
ValueCountFrequency (%)
42.9830241 3
 
0.4%
43.024593 1
 
0.1%
43.1150678 3
 
0.4%
43.1666497 2
 
0.2%
43.4769604 2
 
0.2%
44.6062079 1
 
0.1%
44.8632577 2
 
0.2%
45.7684014 21
2.5%
46.3498308 5
 
0.6%
47.2216548 30
3.6%
ValueCountFrequency (%)
64.543022 2
 
0.2%
62.0274078 1
 
0.1%
61.790039 2
 
0.2%
59.938732 32
3.8%
59.218876 7
 
0.8%
58.6124279 1
 
0.1%
58.6035661 5
 
0.6%
58.5209862 10
 
1.2%
58.02148705 13
1.6%
57.8173923 4
 
0.5%

region_capital_long
Real number (ℝ)

Distinct64
Distinct (%)7.8%
Missing7
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean47.194188
Minimum20.510584
Maximum135.07693
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.6 KiB
2023-01-29T06:06:15.604283image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum20.510584
5-th percentile20.510584
Q139.026104
median49.124227
Q349.124227
95-th percentile86.087121
Maximum135.07693
Range114.56635
Interquartile range (IQR)10.098122

Descriptive statistics

Standard deviation18.947838
Coefficient of variation (CV)0.40148667
Kurtosis7.1832309
Mean47.194188
Median Absolute Deviation (MAD)7.1065399
Skewness2.2095773
Sum38935.205
Variance359.02056
MonotonicityNot monotonic
2023-01-29T06:06:15.664073image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
49.1242266 331
39.8%
20.5105838 74
 
8.9%
40.4075203 40
 
4.8%
37.6174943 33
 
4.0%
30.316229 32
 
3.8%
39.7096061 30
 
3.6%
39.0261044 21
 
2.5%
35.89524162 15
 
1.8%
39.8933705 15
 
1.8%
56.23076653 13
 
1.6%
Other values (54) 221
26.6%
ValueCountFrequency (%)
20.5105838 74
8.9%
28.3343465 4
 
0.5%
30.316229 32
3.8%
31.2757862 10
 
1.2%
32.04718122 4
 
0.5%
34.3668288 3
 
0.4%
34.390007 2
 
0.2%
35.89524162 15
 
1.8%
36.179604 1
 
0.1%
36.2598115 7
 
0.8%
ValueCountFrequency (%)
135.076935 8
1.0%
131.8855768 3
 
0.4%
129.7319787 1
 
0.1%
127.527161 1
 
0.1%
113.500893 5
0.6%
107.5839105 4
0.5%
104.280586 8
1.0%
92.8725147 9
1.1%
86.0871213 5
0.6%
85.963653 1
 
0.1%

Interactions

2023-01-29T06:06:11.096706image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-29T06:05:53.810601image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-29T06:05:55.046058image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-29T06:05:56.040063image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-29T06:05:56.930235image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-29T06:05:58.323257image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-29T06:05:59.318376image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-29T06:06:00.207517image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-29T06:06:01.291292image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-29T06:06:02.257970image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-29T06:06:03.244979image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-29T06:06:04.310042image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-29T06:06:05.296659image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-29T06:06:06.257376image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-29T06:06:07.209911image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-29T06:06:08.173091image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-29T06:06:09.115745image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-29T06:06:10.079199image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-29T06:06:11.149004image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-29T06:05:53.916901image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-29T06:05:55.099600image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-29T06:05:56.088122image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-29T06:05:56.982320image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-29T06:05:58.376832image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-29T06:05:59.365480image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-29T06:06:00.264288image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-29T06:06:01.345419image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-29T06:06:02.312708image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-29T06:06:03.300274image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-29T06:06:04.364226image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-29T06:06:05.341396image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-29T06:06:06.307261image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-29T06:06:07.254856image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-29T06:06:08.226258image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-29T06:06:09.167910image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-29T06:06:10.132453image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-29T06:06:11.199705image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-29T06:05:54.048445image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-29T06:05:55.145873image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-29T06:05:56.135427image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-29T06:05:57.033736image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-29T06:05:58.428771image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-29T06:05:59.409925image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-29T06:06:00.313527image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-29T06:06:01.395201image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-29T06:06:02.363341image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-29T06:06:03.348945image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-29T06:06:04.414894image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-29T06:06:05.384079image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-29T06:06:06.356096image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-29T06:06:07.300420image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-29T06:06:08.277208image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-29T06:06:09.222460image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-29T06:06:10.179966image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-29T06:06:11.247989image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-29T06:05:54.106352image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-29T06:05:55.196098image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-29T06:05:56.180025image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-29T06:05:57.084023image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-29T06:05:58.477518image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-29T06:05:59.456745image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-29T06:06:00.366381image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-29T06:06:01.444885image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-29T06:06:02.414902image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-29T06:06:03.397329image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-29T06:06:04.467506image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-29T06:06:05.432756image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-29T06:06:06.403280image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-29T06:06:07.427057image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-29T06:06:08.322879image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-29T06:06:09.268877image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-29T06:06:10.224958image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-29T06:06:11.304049image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-29T06:05:54.166710image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-29T06:05:55.251125image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
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2023-01-29T06:06:00.056458image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-29T06:06:01.048296image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-29T06:06:02.091581image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-29T06:06:03.082556image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-29T06:06:04.047885image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-29T06:06:05.127884image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-29T06:06:06.107459image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-29T06:06:07.047907image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-29T06:06:08.022162image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-29T06:06:08.959175image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-29T06:06:09.918466image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-29T06:06:10.936974image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-29T06:06:11.954830image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-29T06:05:54.923064image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-29T06:05:55.933476image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-29T06:05:56.833165image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-29T06:05:58.059837image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-29T06:05:59.202964image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-29T06:06:00.105126image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-29T06:06:01.179409image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-29T06:06:02.147273image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-29T06:06:03.136908image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-29T06:06:04.103380image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-29T06:06:05.182795image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-29T06:06:06.155874image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-29T06:06:07.099990image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-29T06:06:08.070943image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-29T06:06:09.014449image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-29T06:06:09.972609image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-29T06:06:10.991002image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-29T06:06:12.016145image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-29T06:05:54.988405image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-29T06:05:55.983965image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-29T06:05:56.879215image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-29T06:05:58.131791image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-29T06:05:59.259491image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-29T06:06:00.158551image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-29T06:06:01.234541image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-29T06:06:02.201449image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-29T06:06:03.189994image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-29T06:06:04.165546image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-29T06:06:05.241816image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-29T06:06:06.209675image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-29T06:06:07.154528image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-29T06:06:08.125006image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-29T06:06:09.061648image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-29T06:06:10.025057image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-29T06:06:11.041725image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-01-29T06:06:15.731356image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
asset_idviewssquareprice_valueshown_by_daysdistanceregion_squarepopulationgrpcriminalitykm_to_mskkm_to_region_capitalcadastral_latcadastral_longregion_latregion_longregion_capital_latregion_capital_longseller_nameregion_capitalregion_capital_geocode
asset_id1.000-0.0890.0650.003-0.0100.0010.028-0.041-0.0510.0340.0730.039-0.1040.112-0.0420.065-0.0580.0710.0000.0000.000
views-0.0891.000-0.1940.2890.778-0.0270.1520.1590.0530.057-0.188-0.0120.041-0.0970.1720.0580.1310.0730.4020.2860.286
square0.065-0.1941.0000.364-0.1620.057-0.0060.0680.117-0.025-0.042-0.099-0.0100.016-0.0560.026-0.019-0.0130.0000.0000.000
price_value0.0030.2890.3641.0000.305-0.1470.0520.1780.1020.008-0.283-0.0760.174-0.4240.159-0.0270.121-0.0320.6670.0000.000
shown_by_days-0.0100.778-0.1620.3051.000-0.1170.0780.1050.031-0.007-0.2250.0850.100-0.1410.1870.0620.1880.0790.4300.4760.476
distance0.001-0.0270.057-0.147-0.1171.0000.2300.0470.1170.1000.1830.0010.0420.1440.0130.1340.0620.1250.2720.0000.000
region_square0.0280.152-0.0060.0520.0780.2301.0000.2790.1480.6540.3950.041-0.0910.520-0.0130.6190.0850.5780.5330.9640.964
population-0.0410.1590.0680.1780.1050.0470.2791.0000.867-0.053-0.0570.003-0.2550.1010.0150.303-0.0870.2810.5900.8560.856
grp-0.0510.0530.1170.1020.0310.1170.1480.8671.000-0.067-0.113-0.010-0.1710.1320.1490.3770.0340.3600.7160.7470.747
criminality0.0340.057-0.0250.008-0.0070.1000.654-0.053-0.0671.0000.2620.0470.0680.2310.0370.3550.1570.3140.4930.9620.962
km_to_msk0.073-0.188-0.042-0.283-0.2250.1830.395-0.057-0.1130.2621.000-0.027-0.4530.687-0.5570.416-0.4850.3870.5080.9320.932
km_to_region_capital0.039-0.012-0.099-0.0760.0850.0010.0410.003-0.0100.047-0.0271.0000.066-0.0270.057-0.0150.0740.0040.5910.4000.400
cadastral_lat-0.1040.041-0.0100.1740.1000.042-0.091-0.255-0.1710.068-0.4530.0661.000-0.3370.976-0.3250.970-0.2350.2210.7680.768
cadastral_long0.112-0.0970.016-0.424-0.1410.1440.5200.1010.1320.2310.687-0.027-0.3371.000-0.3030.992-0.2760.9670.3910.8240.824
region_lat-0.0420.172-0.0560.1590.1870.013-0.0130.0150.1490.037-0.5570.0570.976-0.3031.000-0.0370.9210.0010.4030.8080.808
region_long0.0650.0580.026-0.0270.0620.1340.6190.3030.3770.3550.416-0.015-0.3250.992-0.0371.0000.0560.9900.5810.9500.950
region_capital_lat-0.0580.131-0.0190.1210.1880.0620.085-0.0870.0340.157-0.4850.0740.970-0.2760.9210.0561.0000.0690.4440.9660.966
region_capital_long0.0710.073-0.013-0.0320.0790.1250.5780.2810.3600.3140.3870.004-0.2350.9670.0010.9900.0691.0000.5290.9660.966
seller_name0.0000.4020.0000.6670.4300.2720.5330.5900.7160.4930.5080.5910.2210.3910.4030.5810.4440.5291.0000.4430.443
region_capital0.0000.2860.0000.0000.4760.0000.9640.8560.7470.9620.9320.4000.7680.8240.8080.9500.9660.9660.4431.0001.000
region_capital_geocode0.0000.2860.0000.0000.4760.0000.9640.8560.7470.9620.9320.4000.7680.8240.8080.9500.9660.9660.4431.0001.000

Missing values

2023-01-29T06:06:12.167370image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-01-29T06:06:12.487768image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-01-29T06:06:12.740303image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

asset_idviewspublication_datecontact_nameseller_namesquareprice_valuecadastral_geocoderegion_geocodeshown_by_daysdistanceregion_squarepopulationregion_capitalgrpcriminalitykm_to_mskregion_capital_geocodekm_to_region_capitalcadastral_latcadastral_longregion_latregion_longregion_capital_latregion_capital_long
0814114.01535.015 ноября 2022Контактный Центр БанкаБанк ВТБ (ПАО)NaN48700000.0NaN[55.6711507, 37.2727963]64.884407NaN44329.08542257.0Москва4201.872.923.785924[55.7504461, 37.6174943]NaNNaNNaN55.67115137.27279655.75044637.617494
1641505.02311.031 марта 2022Гаас Александра ГеоргиевнаФизическое лицо25111.0145500000.0[51.94720028246027, 84.88831594503662][51.9931851, 84.9819571]293.8844078.221511167996.02154932.0Барнаул550.0130.83077.808288[53.347402, 83.7784496]172.97112951.94720084.88831651.99318584.98195753.34740283.778450
2169347.0466.018 апреля 2022Брюнель Ева ЖисленовнаФизическое лицо50574.04500000.0[56.77190698765615, 38.88962726762723][56.80208355, 38.64899133747492]275.88440715.08641236177.01205637.0Ярославль560.6105.1137.376612[57.6263877, 39.8933705]112.85241456.77190738.88962756.80208438.64899157.62638839.893371
3899895.01286.025 июня 2022Плесняев Александр АлександровичПлесняев Александр Александрович504500.023450000.0[55.73199800626753, 35.340751735565306][55.506478, 36.0213092]207.88440749.68850744329.08542257.0Москва4201.872.9142.859455[55.7504461, 37.6174943]143.00831455.73199835.34075255.50647836.02130955.75044637.617494
4269183.01057.017 июня 2022Беликин Иван ВладимировичБеликин Иван Владимирович10000.0320000000.0[55.7008175028533, 37.02131098410721][55.6711507, 37.2727963]215.88440716.15901844329.08542257.0Москва4201.872.937.925763[55.7504461, 37.6174943]37.86581655.70081837.02131155.67115137.27279655.75044637.617494
5621582.0812.015 марта 2021Остроухова АллаПАО Сбербанк2400.0136000.0[47.34721090904763, 39.57702065464689][47.370534899999996, 39.46597632166487]674.8844078.780511100967.04192322.0Ростов-на-Дону1446.2104.8945.986855[47.2216548, 39.7096061]17.18884747.34721139.57702147.37053539.46597647.22165539.709606
6763916.0590.015 марта 2021Остроухова АллаПАО Сбербанк2400.0119600.0[47.34776296813592, 39.57636617315176][47.370534899999996, 39.46597632166487]674.8844078.715216100967.04192322.0Ростов-на-Дону1446.2104.8945.919544[47.2216548, 39.7096061]17.26755347.34776339.57636647.37053539.46597647.22165539.709606
7534792.0533.015 марта 2021Остроухова АллаПАО Сбербанк2400.0136000.0[47.34693528804215, 39.577344682351224][47.370534899999996, 39.46597632166487]674.8844078.812996100967.04192322.0Ростов-на-Дону1446.2104.8946.020434[47.2216548, 39.7096061]17.14967447.34693539.57734547.37053539.46597647.22165539.709606
8722101.0584.015 марта 2021Остроухова АллаПАО Сбербанк2400.0103200.0[47.347486693136666, 39.576694736564505][47.370534899999996, 39.46597632166487]674.8844078.747939100967.04192322.0Ростов-на-Дону1446.2104.8945.953239[47.2216548, 39.7096061]17.22811947.34748739.57669547.37053539.46597647.22165539.709606
9255031.0532.015 марта 2021Остроухова АллаПАО Сбербанк2400.0119600.0[47.34611207788003, 39.578309974320206][47.370534899999996, 39.46597632166487]674.8844078.910165100967.04192322.0Ростов-на-Дону1446.2104.8946.120704[47.2216548, 39.7096061]17.03278347.34611239.57831047.37053539.46597647.22165539.709606
asset_idviewspublication_datecontact_nameseller_namesquareprice_valuecadastral_geocoderegion_geocodeshown_by_daysdistanceregion_squarepopulationregion_capitalgrpcriminalitykm_to_mskregion_capital_geocodekm_to_region_capitalcadastral_latcadastral_longregion_latregion_longregion_capital_latregion_capital_long
822881953.0284.021 мая 2021Коваленко Александр АнАО "Россельхозбанк"1577.08297248.0'bool' object is not subscriptable[56.1345574, 38.85192758087219]607.884408NaN29084.01342235.0Владимир440.591.187.684679[56.1288899, 40.4075203]NaNNaNNaN56.13455738.85192856.12889040.407520
823129969.097.021 мая 2021Сысоева ОксанаПАО Сбербанк50200.01400000.0[47.26573931064377, 39.60212825367861][47.370534899999996, 39.46597632166487]607.88440815.546689100967.04192322.0Ростов-на-Дону1446.2104.8955.215166[47.2216548, 39.7096061]9.49911847.26573939.60212847.37053539.46597647.22165539.709606
824817823.0889.021 мая 2021Сотрудник ПАО СбербанкПАО Сбербанк0.0152158.0[55.33725071485237, 45.750579887330844]NaN607.884408NaN76624.03108918.0Нижний Новгород1367.596.0515.357323[56.3264816, 44.0051395]155.21218855.33725145.750580NaNNaN56.32648244.005139
825893392.0273.020 мая 2021Коваленко Александр АнАО "Россельхозбанк"1452.010183939.0[56.219598046418646, 38.82971559603704][56.1345574, 38.85192758087219]608.8844089.56881929084.01342235.0Владимир440.591.191.312190[56.1288899, 40.4075203]98.51710256.21959838.82971656.13455738.85192856.12889040.407520
826595554.0285.020 мая 2021Коваленко Александр АнАО "Россельхозбанк"1543.010822193.0[56.21937001369826, 38.829364808205405][56.1345574, 38.85192758087219]608.8844089.54686829084.01342235.0Владимир440.591.191.280048[56.1288899, 40.4075203]98.53646456.21937038.82936556.13455738.85192856.12889040.407520
827691104.0269.020 мая 2021Коваленко Александр АнАО "Россельхозбанк"1527.010709963.0[56.21913602478085, 38.82900562858026][56.1345574, 38.85192758087219]608.8844089.52440729084.01342235.0Владимир440.591.191.247107[56.1288899, 40.4075203]98.55629256.21913638.82900656.13455738.85192856.12889040.407520
828630263.0279.020 мая 2021Коваленко Александр АнАО "Россельхозбанк"1321.09265136.0[56.220926330893064, 38.83048310378695][56.1345574, 38.85192758087219]608.8844089.70853929084.01342235.0Владимир440.591.191.433379[56.1288899, 40.4075203]98.48328056.22092638.83048356.13455738.85192856.12889040.407520
829177552.0281.020 мая 2021Коваленко Александр АнАО "Россельхозбанк"1676.011755032.0[56.21983629294916, 38.8300820194728][56.1345574, 38.85192758087219]608.8844089.59182429084.01342235.0Владимир440.591.191.345769[56.1288899, 40.4075203]98.49688556.21983638.83008256.13455738.85192856.12889040.407520
830841885.0610.011 мая 2021Нуриев Эдуард РамзиловичАО "Россельхозбанк"150100.0226651.0[55.822853130025955, 52.73092958759532][55.682141200000004, 52.09069161386946]617.88440843.14571167847.04000084.0Казань2469.2102.1946.305150[55.7823547, 49.1242266]226.19719155.82285352.73093055.68214152.09069255.78235549.124227
831419254.0461.022 апреля 2021Самойленко Денис СергеевичПАО Сбербанк10000.08270000.0[57.14073651577907, 33.14393000643601][57.1557583, 33.1013213]636.8844083.07413984201.01226038.0Тверь441.7130.5315.461761[56.8596713, 35.89524161906262]170.09417557.14073733.14393057.15575833.10132156.85967135.895242